Goenka saw a better way: Build a real-time system that could “stream” the sequencing data, analyzing it as it was being generated, like streaming a film on Netflix rather than downloading it to watch later.
To do this, she designed a cloud computing architecture to pull in more processing power. Goenka’s first challenge was to increase the speed at which her team could upload the raw data for processing, by streamlining the requests between the sequencer and the cloud to avoid unnecessary “chatter.” She worked out the exact number of communication channels needed—and created algorithms that allowed those channels to be reused in the most efficient way.
The next challenge was “base calling”—converting the raw signal from the sequencing machine into the nucleotide bases A, C, T, and G, the language that makes up our DNA. Rather than using a central node to orchestrate this process, which is an inefficient, error-prone approach, Goenka wrote software to automatically assign dozens of data streams directly from the sequencer to dedicated nodes in the cloud.
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Then, to identify mutations, the sequences were aligned for comparison with a reference genome. She coded a custom program that triggers alignment as soon as base calling finishes for one batch of sequences while simultaneously initiating base calling for the next batch, thus ensuring that the system’s computational resources are used efficiently.
Add all these improvements together, and Goenka’s approach reduced the total time required to analyze a genome for mutations from around 20 hours to 1.5 hours. Finally, the team worked with genetic counselors and physicians to create a filter that identified which mutations were most critical to a person’s health, and that set was then given a final manual curation by a genetic specialist. These final stages take up to three hours. The technology was close to being fully operational when, suddenly, the first patient arrived.
A critical test
When 13-year-old Matthew was flown to Stanford’s children’s hospital in 2021, he was struggling to breathe and his heart was failing. Doctors needed to know whether the inflammation in his heart was due to a virus or to a genetic mutation that would necessitate a transplant.
His blood was drawn on a Thursday. The transplant committee made its decisions on Fridays. “It meant we had a small window of time,” says Goenka.
Goenka was in Mumbai when the sequencing began. She stayed up all night, monitoring the computations. That was when the project stopped being about getting faster for the sake of it, she says: “It became about ‘How fast can we get this result to save this person’s life?’”
